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Patexia Research
Patent No. US 11170009
Issue Date Nov 9, 2021
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Patent 11170009 - System and a method for resource data classification and management > Claims

  • 1. A method for developing a tool for resource data classification and management, wherein the method is implemented by at least one processor executing program instructions stored in a memory, the method comprising: evaluating, by the processor, deployment probability scores of a plurality of incoming data-records, wherein the evaluation comprises creating one or more rules to analyse and extract information from the plurality of incoming data-records based on a plurality of previous data-records maintained throughout a predefined duration of time using an extreme gradient boosting machine learning technique, and wherein the extracted information comprises details associated with bench period of one or more years for each of the incoming data records;computing, by the processor, a match score of the plurality of incoming data-records in relation to one or more vacancy records, wherein each of the plurality of incoming data-records and the one or more vacancy records are analysed using a first set of rules;determining, by the processor, a bench period associated with each of the plurality of incoming data-records, wherein the bench period is a period for which the incoming data-record is set to hold due to unavailability of vacancy and lack of skills; andcategorizing, by the processor, the plurality of incoming data-records based on a combination of the evaluated deployment probability scores, the computed match score and the bench period for generating a deployment opportunity index for managing resource data, wherein match score corresponding to each of the incoming data records received from a third party server is additionally included for categorizing the plurality of incoming data-records, and wherein the deployment opportunity index is updated based on a change in the bench period.
    • 2. The method as claimed in claim 1, wherein each of the plurality of previous data-records and the plurality of incoming data-records comprise details related to a plurality of parameters, wherein said parameters are selected to include information associated with at least demographics, performance history, and utilization history of resource.
      • 3. The method as claimed in claim 2, wherein the plurality of parameters include name, grade, location, region, latest rating, previous year rating, rating prior to previous year, time since last promotion, utilization in current role, utilization in previous role, billability status, previous role type, technical skills, historical median time taken to fit a vacancy record, and current demand for existing data record.
    • 4. The method as claimed in claim 1, wherein the one or more vacancy records are associated with existing vacancy in the organization, said one or more vacancy records are retrieved from a vacancy database, further wherein each vacancy record comprises information associated with job role technical skills, grade, location, and region.
    • 5. The method as claimed in claim 1, wherein the match score is representative of acceptability of the incoming data-records for one or more vacancy records associated with corresponding vacancies in the organization.
    • 6. The method as claimed in claim 1, wherein the first set of rules comprises: (a) computing a percentage of commonality between the incoming data-record and the vacancy record, wherein the percentage of commonality is computed by determining and analysing skills that are common in the incoming data-record and the vacancy records; (b) computing a proficiency in the determined common skills for the incoming data-records; (c) computing skill adjacency between the incoming data-record and the vacancy record, wherein the skill adjacency is computed by determining similarity between skills of the incoming data-record and the vacancy record; (d) mapping location, grade and region details of the incoming data-record with the vacancy data record; (e) computing the match score of the incoming data-record based on corresponding percentage of commonality, proficiency, skill adjacency, along with location, grade and region details, wherein the data-record having a better combination of percentage of commonality, proficiency, skill adjacency, location, grade and region details has a high match score; and (f) repeating steps (a)-(e) for each incoming data-record.
    • 7. The method as claimed in claim 1, wherein each of the plurality of incoming data-records are categorized into four categories, wherein the incoming data-records having a high deployment probability score, a match score of more than 70% and a bench period ranging between 0-29 days are categorized as easily deployable incoming data-records; the incoming data-records having a high deployment probability score, a match score of 40-70% and a bench period ranging between 0-29 days are categorized as moderately deployable data-records; the incoming data-records having a low deployment probability score, a match score of 40-70% and a bench period ranging between 0-29 days are categorized as the data-records requiring reskilling or upskilling; and the incoming data-records having a low deployment probability score, a match score of less than 40% and a bench period ranging between 0-29 days are categorized as data-records requiring reskilling intervention.
    • 8. The method as claimed in claim 1, wherein the deployment opportunity index is generated for each of the incoming data-records, the deployment opportunity index representing the categories and corresponding probability score, match score and bench period associated with each of the plurality incoming data-records.
    • 9. The method as claimed in claim 1, wherein a list of recommendations are generated for each incoming data-record based on the corresponding category.
  • 10. A system for developing a tool for resource data classification and management, the system comprising: a memory storing program instructions;a processor configured to execute program instructions stored in the memory; anddeployment opportunity evaluation engine in communication with the processor and configured to:evaluate deployment probability scores of a plurality of incoming data-records, wherein the evaluation comprises creating one or more rules to analyse and extract information from the plurality of incoming data-records based on a plurality of previous data-records maintained throughout a predefined duration of time using an extreme gradient boosting machine learning technique, and wherein the extracted information comprises details associated with bench period of one or more years for each of the incoming data records;compute a match score of the plurality of incoming data-records in relation to one or more vacancy records, wherein each of the plurality of incoming data-records and the one or more vacancy records are analysed using a first set of rules;determine a bench period associated with each of the plurality of incoming data-records, wherein the bench period is a period for which the incoming data-record is set to hold due to unavailability of vacancy and lack of skills; andcategorize the plurality of incoming data-records based on a combination of the evaluated deployment probability score, the computed match score and the bench period for generating a deployment opportunity index for managing resource data, wherein match score corresponding to each of the incoming data records received from a third party server is additionally included for categorizing the plurality of incoming data-records, and wherein the deployment opportunity index is updated based on a change in the bench period.
    • 11. The system as claimed in claim 10, wherein each of the plurality of previous data-records and the plurality of incoming data-records comprise details related to a plurality of parameters, wherein said parameters are selected to include information associated with at least demographics, performance history, and utilization history of resource.
      • 12. The system as claimed in claim 11, wherein the plurality of parameters include name, grade, location, region, latest rating, previous year rating, rating prior to previous year, time since last promotion, utilization in current role, utilization in previous role, billability status, previous role type, technical skills, historical median time taken to fit a vacancy record, and current demand for existing data record.
    • 13. The system as claimed in claim 10, wherein the one or more vacancy records associated with existing vacancy in the organization are retrieved from a vacancy database, further wherein each vacancy record comprises information associated with job role technical skills, grade, location, and region.
    • 14. The system as claimed in claim 10, wherein the match score is representative of acceptability of the incoming data-records for one or more vacancy records associated with corresponding vacancies in the organization.
    • 15. The system as claimed in claim 10, wherein the first set of rules comprises: (a) computing a percentage of commonality between the incoming data-record and the vacancy record, wherein the percentage of commonality is computed by determining and analysing skills that are common in the incoming data-record and the vacancy records; (b) computing a proficiency in the determined common skills for the incoming data-records; (c) computing skill adjacency between the incoming data-record and the vacancy record, wherein the skill adjacency is computed by determining similarity between skills of the incoming data-record and the vacancy record; (d) mapping location, grade and region details of the incoming data-record with the vacancy data record; (e) computing the match score of the incoming data-record based on corresponding percentage of commonality, proficiency, skill adjacency, along with location, grade and region details, wherein the data-record having a better combination of percentage of commonality, proficiency, skill adjacency, location, grade and region details has a high match score; and (f) repeating steps (a)-(e) for each incoming data-record.
    • 16. The system as claimed in claim 10, wherein each of the plurality of incoming data-records are categorized into four categories, wherein the incoming data-records having a high deployment probability score, a match score of more than 70% and a bench period ranging between 0-29 days are categorized as easily deployable incoming data-records; the incoming data-records having a high deployment probability score, a match score of 40-70% and a bench period ranging between 0-29 days are categorized as moderately deployable data-records; the incoming data-records having a low deployment probability score, a match score of 40-70% and a bench period ranging between 0-29 days are categorized as the data-records requiring reskilling or upskilling; and the incoming data-records having a low deployment probability score, a match score of less than 40% and a bench period ranging between 0-29 days are categorized as data-records requiring reskilling intervention.
    • 17. The system as claimed in claim 10, wherein the deployment opportunity index is generated for each of the incoming data-records, the deployment opportunity index representing the categories and corresponding probability score, match score and bench period associated with each of the plurality incoming data-records.
    • 18. The system as claimed in claim 10, wherein a list of recommendations are generated for each incoming data-record based on the corresponding category.
  • 19. A computer program product comprising: a non-transitory computer-readable medium having computer-readable program code stored thereon, the computer-readable program code comprising instructions that, when executed by a processor, cause the processor to: evaluate deployment probability scores of a plurality of incoming data-records, wherein the evaluation comprises creating one or more rules to analyse and extract information from the plurality of incoming data-records based on a plurality of previous data-records maintained throughout a predefined duration of time using an extreme gradient boosting machine learning technique, and wherein the extracted information comprises details associated with bench period of one or more years for each of the incoming data records;compute a match score of the plurality of incoming data-records in relation to one or more vacancy records, wherein each of the plurality of incoming data-records and the one or more vacancy records are analysed using a first set of rules;determine a bench period associated with each of the plurality of incoming data-records, wherein the bench period is a period for which the incoming data-record is set to hold due to unavailability of vacancy and lack of skills; andcategorize the plurality of incoming data-records based on a combination of the evaluated deployment probability score, the computed match score and the bench period for generating a deployment opportunity index for managing resource data, wherein match score corresponding to each of the incoming data records received from a third party server is additionally included for categorizing the plurality of incoming data-records, and wherein the deployment opportunity index is updated based on a change in the bench period.
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